System, method, and recording medium for regular rule learning

ABSTRACT

A regular rule learning system, including an analyzing circuit configured to analyze a corpus of sentences to find semantic relationships between sentence constituents that are responsible for specific senses of words in that sentence by describing the semantic relationships and grammatical relations that are actuated in the sentence.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S. patentapplication Ser. No. 15/791,791 filed on Oct. 24, 2017, which is aContinuation Application of U.S. patent application Ser. No. 15/087,032filed on Mar. 31, 2016, now U.S. Pat. No. 9,836,454 issued on Dec. 5,2017, the entire contents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to a regular rule learningsystem, and more particularly, but not by way of limitation, to a systemfor learning regular structure over natural languages with variablelexical classes.

Mapping verbal usage to regular expressions have been considered.Conventional techniques proved that regular expressions extractedcorpora can be learned and they are instrumental to a wide range ofapplications involving semantic processing. Such conventional techniquesinvolve using of ontological categories.

Other conventional techniques rely on bags of words (i.e., a fixednumber of lexical features) in order to predict the meaning of inputcontent.

However, there is a technical problem with the conventional techniquesthat the ontological categories hinder the accuracy of the proposedmethod and the reliance on bag of words can limit the prediction ofambiguous terms.

SUMMARY

The inventors have considered the technical solution to the technicalproblem by using variable lexical classes which supersede the use ofontological classes. In proceeding so, the inventors have considered toindividuate the meaning relevant context for word sense disambiguationfor verbal phrase and to represent it under the form of a pattern suchthe accuracy and the applicability of method increases significantly.Further, instead of relying on bag of words as in conventionaltechniques, the proposed technical solution by the inventors identifieswhat feature of the context contribute to the meaning of its componentsin that the patterns represent lexical information gather by clusteringspecific context. At divergence with previous techniques as realized bythe inventors, which assume a fix number of lexical feature, thetechnical solution allows an indefinite number of lexical features to beused.

In an exemplary embodiment, the present invention can provide a regularrule learning system including an analyzing circuit configured toanalyze a corpus of sentences stored in a database to discover lexicalfeatures and conjunctively create a regular set of rules based on thediscovered lexical features and syntactical features.

Further, in another exemplary embodiment, the present invention canprovide a regular rule learning method including analyzing a corpus ofsentences stored in a database to discover lexical features andconjunctively create a regular set of rules based on the discoveredlexical features and syntactical features.

Even further, in another exemplary embodiment, the present invention canprovide a non-transitory computer-readable recording medium recording aregular rule learning program, the program causing a computer toperform: analyzing a corpus of sentences stored in a database todiscover lexical features and conjunctively create a regular set ofrules based on the discovered lexical features and syntactical features.

There has thus been outlined, rather broadly, an embodiment of theinvention in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional exemplaryembodiments of the invention that will be described below and which willform the subject matter of the claims appended hereto.

It is to be understood that the invention is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. The invention is capable of embodiments in addition tothose described and of being practiced and carried out in various ways.Also, it is to be understood that the phraseology and terminologyemployed herein, as well as the abstract, are for the purpose ofdescription and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary aspects of the invention will be better understood fromthe following detailed description of the exemplary embodiments of theinvention with reference to the drawings.

FIG. 1 exemplarily shows a block diagram illustrating a configuration ofa regular rule learning system 100.

FIG. 2 exemplarily shows a high level flow chart for a regular rulelearning method 200.

FIG. 3 depicts a cloud computing node 10 according to an embodiment ofthe present invention.

FIG. 4 depicts a cloud computing environment 50 according to anotherembodiment of the present invention.

FIG. 5 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The invention will now be described with reference to FIGS. 1-5, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawing are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity. Exemplary embodiments are provided below forillustration purposes and do not limit the claims.

With reference now to FIG. 1, the regular rule learning system 100includes an analyzing circuit 101, a receiving circuit 102, and apredicting circuit 103. The regular rule learning system 100 includes aprocessor 180 and a memory 190, with the memory 190 storing instructionsto cause the processor 180 to execute each circuit of regular rulelearning system 100. The processor and memory may be physical hardwarecomponents, or a combination of hardware and software components.

Although the regular rule learning system 100 includes various circuits,it should be noted that a regular rule learning system can includemodules in which the memory 190 stores instructions to cause theprocessor 180 to execute each module of regular rule learning system100.

Also, each circuit can be a stand-alone device, unit, module, etc. thatcan be interconnected to cooperatively produce a transformation to aresult.

With the use of these various circuits, the regular rule learning system100 may act in a more sophisticated and useful fashion, and in acognitive manner while giving the impression of mental abilities andprocesses related to knowledge, attention, memory, judgment andevaluation, reasoning, and advanced computation. That is, a system issaid to be “cognitive” if it possesses macro-scaleproperties—perception, goal-oriented behavior, learning/memory andaction—that characterize systems (i.e., humans) that all agree arecognitive.

Cognitive states are defined as functions of measures of a user's totalbehavior collected over some period of time from at least one personalinformation collector (including musculoskeletal gestures, speechgestures, eye movements, internal physiological changes, measured byimaging circuits, microphones, physiological and kinematic sensors in ahigh dimensional measurement space) within a lower dimensional featurespace. In one exemplary embodiment, certain feature extractiontechniques are used for identifying certain cognitive and emotionaltraits. Specifically, the reduction of a set of behavioral measures oversome period of time to a set of feature nodes and vectors, correspondingto the behavioral measures' representations in the lower dimensionalfeature space, is used to identify the emergence of a certain cognitivestate(s) over that period of time. One or more exemplary embodiments usecertain feature extraction techniques for identifying certain cognitivestates. The relationship of one feature node to other similar nodesthrough edges in a graph corresponds to the temporal order oftransitions from one set of measures and the feature nodes and vectorsto another. Some connected subgraphs of the feature nodes are hereinalso defined as a cognitive state. The present application alsodescribes the analysis, categorization, and identification of thesecognitive states by means of further feature analysis of subgraphs,including dimensionality reduction of the subgraphs, for example bymeans of graphical analysis, which extracts topological features andcategorizes the resultant subgraph and its associated feature nodes andedges within a subgraph feature space.

Although as shown in FIGS. 3-5 and as described later, the computersystem/server 12 is exemplarily shown in cloud computing node 10 as ageneral-purpose computing circuit which may execute in a layer theregular rule learning system 100 (FIG. 5), it is noted that the presentinvention can be implemented outside of the cloud environment.

The analyzing circuit 101 analyzes the corpus of sentences 130 stored inthe database 120 to find a set of rules that is regular. That is, thecorpus of sentences 130 include two classes of a syntactical class and alexical class. The syntactical classes are a given parameter. Theanalyzing circuit 101 identifies the lexical features around ambiguouswords in the corpus of sentences 130 to give meaning to the ambiguouswords. The analyzing circuit discovers the lexical features of thecorpus of sentences. In other words, the lexical features are an unknownparameter that the analyzing circuit 101 discovers. The analyzingcircuit 101 further uses the structure of the sentence found from thesyntactical class that is given (i.e., subject, verb, etc.) inconjunction with the analyzed lexical features to create a regular rulesuch that semantical meaning for the sentence is determined.

The analyzing circuit 101 discovers the lexical features of the corpusof sentences 130 based on lattice algebra to infer automatically fromthe corpus of sentences 130 and the set of concepts that are required.The analyzing circuit 101 expresses the difference between differentsenses via the concepts and their syntactic positions. In other words,the analyzing circuit 101 determines relationships between senses ofwords in the corpus of sentences 130 such that it is determined whateach word contributes to the entire sentence (i.e., the lexicalfeatures).

In theoretical computer science and formal language theory, a regularlanguage (also called a rational language or a regular rule) is a formallanguage that can be expressed using a regular expression, in the strictsense of the latter notion used in theoretical computer science. Aregular language can be defined as a language recognized by a finiteautomaton. The equivalence of regular expressions and finite automata isknown as Kleene's theorem.

The analyzing circuit 101 creates a plurality of regular rules based onthe corpus of sentences 130 in the database 120. It is noted that aregular rule does not depend on history or on applying any other rules.That is, the regular rules can be used to predict the meaning of a userinput without referring to the database 120 and independently of anyother rule.

The analyzing circuit 101 uses the pseudo algorithm described hereafterto determine the lexical features and create the regular rules.

The analyzing circuit 101 receives a corpus “C” and parses the corpus“C” (Step 1). For each noun, the analyzing circuit determines the mostfrequent verbs that co-occur with it (Step 2). Next, the analyzingcircuit 101 considers the set of nouns that are conceptually equivalentwith a set of verbs for Formal Conceptual Analysis definition of aconcept (Step 3). Following Step 3, the analyzing circuit 101 clustersthe verbs and the nouns in a concept such that a Wordnet distance isminimal and the concept is maximal (Step 4). To each cluster, theanalyzing circuit 101 assigns a generic lexical feature named “LF1”,“LF2”, . . . , “LFn” (Step 5). The analyzing circuit 101 builds thelattice of concepts as Formal Conceptual Analysis lattice of concepts(Step 6). Then, the analyzing circuit 101 compares the lattice withontology as a statistics measure on partial order (Step 7). Theanalyzing circuit 101 then returns to step 4 and determines if thedifferences are bigger than a given threshold when compared to theWordnet. If not, then the Analyzing circuit outputs a set of lexicalclasses associated with the nouns as “LF1”, “LF2”, . . . , “LFn” (Step9).

The receiving circuit 102 receives a user input 140 of a sentence orphrase.

Based on the user input 140 received by the receiving circuit 102, thepredicting circuit 103 predicts the semantic meaning of the user input140 according to the plurality of regular rules created by the analyzingcircuit.

Therefore, the analyzing circuit 101 identifies the regular rules suchthat the semantic meaning of the word “see”, which can have an ambiguousmeaning, of a user input 140 can be predicted by the predicting circuit103. The prediction circuit 103 can predict a natural language responseto the user input based on the semantical meaning.

FIG. 2 shows a high level flow chart for a method 200 for regular rulelearning.

Step 201 analyzes the corpus of sentences 130 stores in the database 120to find a set of rules that is regular. That is, Step 201 identifies thelexical features around ambiguous words in the corpus of sentences 130to give meaning to the ambiguous words. Step 201 discovers the lexicalfeatures of the corpus of sentences. Step 201 further uses the structureof the sentence found from the syntactical class that is given (i.e.,subject, verb, etc.) in conjunction with the analyzed lexical featuresto create a regular rule such that semantical meaning for the sentenceis determined.

It is noted that Step 201 is broken down into nine sub-steps todetermine the set of lexical classes associated with nouns.

That is, Step 201 receives an corpus “C” and parses the corpus “C” (Step1). For each noun, the Step 201 determines the most frequent verbs thatco-occur with it (Step 2). Next, the Step 201 considers the set of nounsthat are conceptually equivalent with a set of verbs for FormalConceptual Analysis definition of a concept (Step 3). Following Step 3,Step 201 clusters the verbs and the nouns in a concept such that aWordnet distance is minimal and the concept is maximal (Step 4). To eachcluster, Step 201 assigns a generic lexical feature named “LF1”, “LF2”,. . . , “LFn” (Step 5). Step 201 builds the lattice of concepts asFormal Conceptual Analysis lattice of concepts (Step 6). Then, Step 201compares the lattice with ontology as a statistics measure on partialorder (Step 7). Step 201 then returns to step 4 and determines if thedifferences are bigger than a given threshold when compared to theWordnet. If not, then the Step 201 outputs a set of lexical classesassociated with the nouns as “LF1”, “LF2”, . . . , “LFn” (Step 9).

Step 202 receives a user input 140.

Step 203 predicts the semantic meaning of the user input 140 accordingto the plurality of regular rules created by Step 201.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 3, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10, there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop circuits, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or circuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

As shown in FIG. 3, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing circuit. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externalcircuits 14 such as a keyboard, a pointing circuit, a display 24, etc.;one or more circuits that enable a user to interact with computersystem/server 12; and/or any circuits (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing circuits. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,circuit drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 4, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 8 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 4) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, more particularly relative to thepresent invention, the regular rule learning system 100 describedherein.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A regular rule learning system, comprising: aprocessor; and a memory, the memory storing instructions to cause theprocessor to execute: an analyzing circuit configured to analyze acorpus of sentences to discover lexical features of the sentences and tofind semantic relationships between sentence constituents that areresponsible for specific senses of words in that sentence by describingthe semantic relationships and grammatical relations that are actuatedin the sentence, wherein the lexical features are unknown prior to theanalyzing circuit discovering the lexical features.
 2. The system ofclaim 1, wherein the analyzing circuit further analyzes the corpus ofsentences stored in a database to discover the lexical features of thesentences and conjunctively create a set of regular rules based on thediscovered lexical features and syntactical features of the sentences.3. The system of claim 2, wherein the syntactical features of the corpusof sentences are given.
 4. The system of claim 2, wherein the lexicalfeatures are calculated from the corpus of sentences stored in thedatabase prior to the syntactical features being given to the corpus ofsentences.
 5. A regular rule learning method, comprising: analyzing acorpus of sentences to discover lexical features of the sentences and tofind semantic relationships between sentence constituents that areresponsible for specific senses of words in that sentence by describingthe semantic relationships and grammatical relations that are actuatedin the sentence, wherein the lexical features are unknown prior to theanalyzing discovering the lexical features.
 6. The method of claim 5,wherein the analyzing further analyzes the corpus of sentences stored ina database to discover the lexical features of the sentences andconjunctively create a set of regular rules based on the discoveredlexical features and syntactical features of the sentences.
 7. Themethod of claim 6, wherein the lexical features are calculated from thecorpus of sentences stored in the database prior to the syntacticalfeatures being given to the corpus of sentences.
 8. A non-transitorycomputer-readable recording medium recording a regular rule learningprogram, the program causing a computer to perform: analyzing a corpusof sentences to discover lexical features of the sentences and to findsemantic relationships between sentence constituents that areresponsible for specific senses of words in that sentence by describingthe semantic relationships and grammatical relations that are actuatedin the sentence, wherein the lexical features are unknown prior to theanalyzing discovering the lexical features.
 9. The non-transitorycomputer-readable recording medium of claim 8, wherein the analyzingfurther analyzes the corpus of sentences stored in a database todiscover the lexical features of the sentences and conjunctively createa set of regular rules based on the discovered lexical features andsyntactical features of the sentences.
 10. The non-transitorycomputer-readable recording medium of claim 9, wherein the lexicalfeatures are calculated from the corpus of sentences stored in thedatabase prior to the syntactical features being given to the corpus ofsentences.
 11. The system of claim 1, wherein the set of regular rulespredicts a meaning of a user input in the corpus of sentences withoutreferring to a database and independently of any other rule.
 12. Themethod of claim 5, wherein the set of regular rules predicts a meaningof a user input in the corpus of sentences without referring to adatabase and independently of any other rule.
 13. The non-transitorycomputer-readable recording medium of claim 9, wherein the set ofregular rules predicts a meaning of a user input in the corpus ofsentences without referring to a database and independently of any otherrule.